Automatic identification of functional clusters in fMRI data using spatial dependence

Sai Ma, Nicolle M. Correa, Xi Lin Li, Tom Eichele, Vince D. Calhoun, Tlay Adali

Research output: Contribution to journalArticlepeer-review

Abstract

In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependencemutual informationamong spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest.

Original languageEnglish (US)
Article number6009176
Pages (from-to)3406-3417
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume58
Issue number12 PART 1
DOIs
StatePublished - Dec 2011

Keywords

  • Functional magnetic resonance imaging (fMRI)
  • independent component analysis (ICA)
  • multidimensional independent component analysis (MICA)
  • spatial dependence

ASJC Scopus subject areas

  • Biomedical Engineering

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